Encyclopedia of Environmental Science and Engineering, Volume I and II

(Ben Green) #1

REMOTE SENSING 1063


photographs to serve as base maps for other applications.
Radar interferometry is also being used on an experimental
basis for topographic mapping.

IMAGE INTERPRETATION AND ANALYSIS

Many environmental applications of remote sensing rely
solely on visual image interpretation. In many cases, visual
analysis is improved by stereo viewing of overlapping pairs of
images. Increasingly, however, some degree of digital image
processing is used to enhance and analyze remote sensing
data. Simple image enhancement techniques include data
stretches, arithmetic operations such as ratioing and differenc-
ing, statistical transformations such as principal components
analysis, and image convolution, filtering, and edge detec-
tion. More complex image processing techniques include
automated land use/land cover classification of images using
spectral signatures representing different land cover types.^8
Most remote sensing applications require the collection
of some form of reference data or “ground truth,” which is
then related to features or patterns in the imagery. For exam-
ple, pixels in a remotely-sensed hyperspectral image might
be compared to a series of mineral spectra acquired from
ground samples. Ground measurements of soil moisture,
crop productivity, or forest leaf-area index (LAI) could be
related to observed reflectance in a satellite image using
linear regression. Often, ground truth locations are estab-
lished using the Global Positioning System (GPS) to facili-
tate the relation to a georeferenced image.
One significant advantage of digital remotely-sensed
imagery, whether collected electronically or as scanned pho-
tographs, is the ability to use digital data in a geographic
information system (GIS). Once a digital image has been
georeferenced, it can be combined with a variety of other
types of spatial data. This combination of image and non-
image data can be used for a wide range of purposes from
simple map updates to complex spatial analysis. 9,10,11,12

Remote sensing is a rapidly changing field, with more
than twenty new satellite systems scheduled for launching
in the next decade. Major sources of new data will be high-
resolution (approximately 1 m) commercial systems and
the various sensors comprising the Earth Observing System
(EOS).

REFERENCES


  1. Lillesand, T.M. and R.W. Kiefer, 1994. Remote sensing and image
    interpretation, John Wiley and Sons, Inc., New York.

  2. Mausel, P.W., J.H. Everitt, D.E. Escobar and D.J. King. 1992. Airborne
    videography: current status and future perspectives. Photogrammetric
    Engineering and Remote Sensing, vol. 58, no. 8, pp. 1189–1195.

  3. Meisner, D.E. 1986. Fundamentals of airborne video remote sensing.
    Remote Sensing of Environment, vol. 19, no. 1, pp. 63–79.

  4. Vane, G. and A. Goetz, 1993. Terrestrial imaging spectrometry: current
    status, future trends. Remote Sensing of Environment, vol. 44, no. 2/3,
    pp. 117–126.

  5. Waring, R.H., J. Way, E.R. Hunt, Jr., L. Morrissey, K. Jon Ranson,
    J.F. Weishampel, R. Oren and S.E. Franklin, 1995. Imaging radar for
    ecosystem studies. Bio-Science, vol. 45, no. 10, pp, 715–723.

  6. Way, J. and E.A. Smith, 1991. Synthetic aperture radar systems and
    their progression to the EOS SAR, IEEE Transactions on Geosciences
    and Remote Sensing, vol.29, no. 6, pp. 962–985.

  7. Elachi, C., 1988. Spaceborne Radar Remote Sensing, IEEE Press,
    New York.

  8. Jensen, J.R., 1986. Introductory Digital Image Processing: A Remote
    Sensing Perspective, Prentice-Hall, Englewood Cliffs, NJ.

  9. Pequet, D.J. and D.F. Marble, Eds. 1990. Introductory Readings in
    Geographic Information Systems, Taylor and Francis, New York.

  10. Star, J. and J. Estes. 1990. Geographic Information Systems, Prentice-Hall,
    Englewood Cliffs, NJ.

  11. K. Hsu, X. Gao, S. Sooroshian and H.V. Gupta, 1997. Rainfall estima-
    tion from remotely sensed information using artificial neural networks.
    J. Appl. Meteorl., 36, 1176–1190.

  12. Sooroshian, S., S.K. Shu, X. Gao, H. Gupta, B. Imam and D. Braithwaite,
    2000. Evaluation of the PERSIANN system satellite-based estimates of
    tropical rainfall, Bull. Am. Hydrometeorol. Soc., 81, 2035–2046.


JONATHAN CHIPMAN
University of Wisconsin

C018_004_r03.indd 1063C018_004_r03.indd 1063 11/18/2005 11:05:36 AM11/18/2005 11:05:36 AM

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